Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services
Año:2010
Áreas de investigación
  • Inteligencia artificial,
  • Ciencias de la computación y tecnología informática
Datos
Descripción
The cloud computing model aims to make largescale data-intensive computing affordable even for users with limited financial resources, that cannot invest into expensive infrastructures necesssary to run them. In this context, MapReduce is emerging as a highly scalable programming paradigm that enables high-throughput data-intensive processing as a cloud service. Its performance is highly dependent on the underlying storage service, responsible to efficiently support massively parallel data accesses by guaranteeing a high throughput under heavy access concurrency. In this context, quality of service plays a crucial role: the storage service needs to sustain a stable throughput for each individual accesss, in addition to achieving a high aggregated throughput under concurrency. In this paper we propose a technique to address this problem using component monitoring, application-side feedback and behavior pattern analysis to automatically infer useful knowledge about the causes of poor quality of service and provide an easy way to reasonin about potential improvements. We apply our proposal to BlobSeer, a representative data storage service specifically designed to achieve high aggregated throughputs and show through extensive experimentation substantial improvements in the stability of individual data read accesses under MapReduce workloads.
Internacional
Si
Nombre congreso
IEEE CloudCom 2010
Tipo de participación
960
Lugar del congreso
Indianapolis, US
Revisores
Si
ISBN o ISSN
978-1-4244-9405-7
DOI
10.1109/CloudCom.2010.33
Fecha inicio congreso
30/11/2010
Fecha fin congreso
03/12/2010
Desde la página
304
Hasta la página
311
Título de las actas
Proceedings of CloudCom'2010
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Maria de los Santos Perez Hernandez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group (LIA). Laboratorio Inteligencia Artificial. Grupo de Ingeniería Ontológica
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